Deep Learning techniques have been extensively used to deal with tasks related to signal processing and computer vision, such as feature selection [1]} [2]}, face [3]} [4]} and image reconstruction [5]}, multimodal learning [6]}, and topic modeling [7]}, among others. Despite the outstanding results obtained by these models, an intrinsic constraint associated with deep architectures is related to their complexity, which can become an insurmountable problem due to the high number of hyperparameters one must deal with. The present work focuses on this problem.
